Creative work systems and methods thereof

ABSTRACT

A computer-implemented method for measuring cognitive load of a user creating a creative work in a creative work system, may include generating at least one verbal statement capable of provoking at least one verbal response from the user, prompting the user to vocally interact with the creative work system by vocalizing the at least one generated verbal statement to the user via an audio interface of the creative work system, and obtaining the at least one verbal response from the user via the audio interface, and determining the cognitive load of the user based on the at least one verbal response obtained from the user, wherein generating the at least one verbal statement is based on at least one predicted verbal response suitable for determining the cognitive load of the user.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority from European patent application No.21305072.7, filed on Jan. 21, 2021, the contents of which are herebyincorporated herein in their entirety by this reference.

TECHNICAL FIELD

This specification relates to a computer-implemented method formeasuring cognitive load of a user creating a creative work in acreative work system, and to a creative work system for measuringcognitive load of a user creating a creative work.

BACKGROUND

Cognitive load is a measure of the cognitive effort a human being isputting into her or his current activity or task. It is based on theamount of working memory resources in the brain being used. Users with ahigher cognitive load may find it challenging to produce creative work,this problem is intensified for users with limited experience or youngusers, e.g., children. Service providers face significant technicaldifficulties in determining the cognitive load of the users while theyare producing creative work.

Recent research shows that the cognitive load of an individual can belearnt from analyzing vocal features in her or his speech. In fact, theproperties of speech features such as phonemes, pitch variations, andpseudo-syllables can be used to determine the current cognitive load ofthe speaker. Furthermore, the cognitive load can also be determined byanalyzing a video sequence (e.g. based on eye tracking) of theindividual.

Creative activities may lead to increased cognitive load of the creator.It is known that positive feedback to creative tasks can reduce thecognitive load of the creator.

SUMMARY

According to a first aspect, there is provided a computer-implementedmethod for measuring cognitive load of a user creating a creative workin a creative work system. The method comprises generating at least oneverbal statement capable of provoking at least one verbal response fromthe user. The method further comprises prompting the user to vocallyinteract with the creative work system by vocalizing the at least onegenerated verbal statement to the user via an audio interface of thecreative work system. The method further comprises obtaining the atleast one verbal response from the user via the audio interface. Themethod further comprises determining the cognitive load of the userbased on the at least one verbal response obtained from the user.Generating the at least one verbal statement is based on at least onepredicted verbal response suitable for determining the cognitive load ofthe user.

According to a second aspect, there is provided a creative work systemfor measuring cognitive load of a user creating a creative work. Thecreative work system comprises a user interface comprising an audiointerface. The creative work system is configured to run the methodaccording to the first aspect (or an embodiment thereof).

Dependent embodiments of the aforementioned aspects are given in thedependent claims and explained in the following description, to whichthe reader should now refer.

The method of the first aspect (or an embodiment thereof) and thecorresponding creative work system of the second aspect (or anembodiment thereof) are directed towards engaging an individual (viz. acreator or user) creating a creative work to vocally interact with thecreative work system and, as an example, towards providing feedback(i.e. tailored responses), in particular positive or useful feedback, tothe individual based on the cognitive load measured by analyzing thespeech of the individual.

Measuring cognitive load can be used to assist the user of the creativework system with creating the creative work. Such may be conducive toengaging the user with the creative work or task and to reducingfrustration. As an example, an (infant) user (e.g. a pupil or student)may be guided through creating the creative work system. In cases, wherecurrently measured cognitive load is rather high, the creative worksystem may try to alleviate the current task (e.g. drawing an apple) forthe (infant) user by applying means known in cognitive psychology (e.g.giving encouragement or praise). This can be used for autodidacticlearners or when supervision (e.g. a teacher) is out of reach, thelatter a circumstance typically encountered during homework and/orsilent study sessions. In particular, when further applyingstate-of-the-art artificial intelligence algorithms capable of acquiringat least a basic understanding of the semantics of the user's speechand/or visually capturing (e.g. using a camera) and interpreting thecreative work as it progresses, the creative work system can be used tosupervise the user online. As an example, in case, the creative worksystem figures out that the (infant) user has a hard time drawing anapple it may offer a picture of an apple or a video tutorial on how todraw an apple. Hence, the creative work system equipped with the methodof the first aspect can be used in self-study, teaching and/oreducation.

Conventionally or often, a creative work is assessed or evaluatedlargely based on a final state the creative work is in after itscompletion. Additionally, some more outer factors such as a duration ofcreating the creative work can be taken into account. On the other hand,recording the cognitive load, and e.g. the corresponding contemporaneousfeatures, as the creation of the creative work progresses can bebeneficial in that the record provides a handle to analyze the creativework even after its completion. Again, such can be used in self-study,teaching and/or education. As an example, a teacher may not have thetime to supervise all pupils at once. On the other hand, if need be, theteacher may resort to the record corresponding to the creative work of apupil in order to assist the pupil on how make an improvement next time.

The method of the first aspect (or an embodiment thereof) comprisesgenerating verbal statements capable of provoking verbal (i.e. can bevocalized by a speaker or a human being) responses from the user thatare likely to be rich in information relevant and useful for determiningthe cognitive load. Such likelihoods are computed based on one or morecandidate verbal statements and corresponding predicted verbalresponses. In so doing, the measurement of cognitive load can beimproved. In addition, vocalized verbal responses are less likely tobore the user, again reducing frustration and increasing engagement ofthe user.

The creative work may comprise or be an artwork, as an example, a visualartwork. An artwork is an artistic creation of aesthetic value and/orbeauty. A visual artwork refers to or comprises an artwork that isvisible (e.g. to human beings) and/or tangible, or that features one ormore physical forms of visual art. A visual artwork refers to orcomprises visual fine art, decorative art and/or applied art. However,aesthetic value and/or beauty may be relative in that what an artwork ismay e.g. depend on the user, in particular on his or her age. As anexample, a child may want to be guided through realistically drawing arather difficult object, such as e.g. an elephant. On the other hand, asanother example, a non-infant (tutorial user) may be more interested inpainting a holiday picture or a still life in terms of a well-knownartistic style (such as e.g. neoclassicism, impressionism,expressionism, cubism, surrealism, . . . ). The visual artwork maycomprise or be a painting. Alternatively, or in addition, the visualartwork may comprise or be a drawing. Alternatively, or in addition, thevisual artwork may comprise or be a handwriting, in particular (a)calligraphy. Alternatively, the visual artwork may comprise or be a 3Dobject. In fact, the 3D object may e.g. comprise or be a sculpture (e.g.of clay, wood, or stone) or a handicraft work (e.g. of paper orcardboard). Alternatively, or in addition, the artwork may comprise anon-visual artwork or an artwork where the visual aspect is subordinate.Such an artwork may comprise or be a musical composition or rehearsing apiece of music. Alternatively, or in addition, the artwork may comprisewriting poetry (e.g. a poem, figuring out how hard it was to findcertain rhymes) or prose (e.g. a short story or novel).

The creative work must not comprise an artwork. Instead, the creativework may be intellectual. The creative work may comprise or be a writingtask. Alternatively, or in addition the creative work may comprise orconsist in inventing a concept/scheme or developing a solution to aproblem.

Embodiments without a camera (apart from being cheaper) may be perceivedby a user as less invasive in terms of privacy.

FIGURE DESCRIPTION

FIG. 1a schematically illustrates a computer-implemented methodaccording to the first aspect (or an embodiment thereof) for measuringcognitive load of a user creating a creative work in a creative worksystem.

FIG. 1b schematically illustrates an embodiment of thecomputer-implemented method according to the first aspect for measuringcognitive load of a user creating a creative work in a creative worksystem.

FIG. 2a schematically illustrates an embodiment of thecomputer-implemented method according to the first aspect for measuringcognitive load of a user creating a creative work in a creative worksystem.

FIG. 2b schematically illustrates an embodiment of thecomputer-implemented method according to the first aspect for measuringcognitive load of a user creating a creative work in a creative worksystem.

FIG. 3 schematically illustrates a creative work system for measuringcognitive load of a user creating a creative work according to thesecond aspect (or an embodiment thereof).

FIG. 4a illustrates creating a creative work in a creative work system.

FIG. 4b illustrates an example dialogue according to thecomputer-implemented method of the first aspect (or an embodimentthereof).

FIG. 5a illustrates an example of a tailored response based on cognitiveload and a contemporaneous feature.

FIG. 5b illustrates an example of a tailored response provoking a thirdresponse from the user and running a third response algorithm based onthe third response from the user feeding back information related tocognitive load and a corresponding contemporaneous feature.

FIG. 5c illustrates an example of a tailored response comprising arecord (e.g. after completion of the creative work) for more than onecognitive load and corresponding contemporaneous features.

FIG. 6 shows an example machine learning training flow chart.

FIG. 7 illustrates an implementation of a general computer system thatmay execute techniques presented herein.

DETAILED DESCRIPTION

Implementations described herein take advantage of modern technologyinfrastructure to measure the semantics of a user's speech to determinethe cognitive load of a user. For example, the present disclosureleverages a machine learning model to automatically determine that acognitive load is high for the user, e.g., based upon a verbal responseof the user, and may try to alleviate the current task of the user byproviding supervision. Such process of simply analyzing the vocalfeatures to determine the current cognitive load of the users reducesthe amount of computing resources used.

The method 100 of the first aspect (or an embodiment thereof) and thecorresponding creative work system 200 of the second aspect (or anembodiment thereof) are directed towards engaging 30, 50 a user creatinga creative work 10 to vocally interact 31, 51 with the creative worksystem 200. As an example, and as illustrated in FIG. 4a-b , thecreative work 10 may consist in drawing an apple 40 on a sheet of paper.The method may also provide feedback (i.e. tailored responses), inparticular positive or useful feedback, to the user based on thecognitive load 20 measured by analyzing the speech of the individual.FIG. 4b illustrates an example dialogue aimed at engaging the user tovocally interact in order to measure her or his cognitive load 20 andhave her or him reveal what she or he is currently drawing (the apple, acontemporaneous feature 40).

FIG. 5a illustrates an example of a tailored response 60 based on thecognitive load 20 and the contemporaneous feature 40 (the apple). Notethat the method may further be configured to elicit (e.g. by asking“What's your name?” and analyzing the corresponding response) and usethe name of the user (Jonny).

FIG. 5b illustrates another example of a tailored response 60 provoking(e.g. offering a video tutorial on how to draw an apple) a thirdresponse 62 (“Yes, please.”) from the user and running a third responsealgorithm based on the third response 62 from the user feeding back 180information related to the cognitive load 20 and a correspondingcontemporaneous feature 40.

FIG. 5c illustrates yet another example of a tailored response 60comprising a record or a report (e.g. after completion of the creativework 10) for more than one cognitive load and correspondingcontemporaneous features.

The computer-implemented method 100 for measuring cognitive load 20 (ormental effort) of the user creating a creative work 10 in a creativework system 200, comprises generating 110 at least one verbal statement30 capable of provoking at least one verbal response 31 from the user.The method 100 further comprises prompting 120 the user to vocallyinteract with the creative work system by vocalizing 121 the at leastone generated verbal statement 30 to the user via an audio interface 211of the creative work system. The method 100 further comprises obtaining130 the at least one verbal response 31 from the user via the audiointerface. The method further comprises determining 140 the cognitiveload 20 of the user based on the at least one verbal response 31obtained from the user. Generating 110 the at least one verbal statement30 is based on at least one predicted verbal response 32 suitable fordetermining the cognitive load 20 of the user. The computer-implementedmethod 100 is schematically illustrated in FIG. 1a -b.

The method 100 may further comprise determining 150 (the) at least onecontemporaneous feature 40 of the creative work 10 based on the at leastone obtained verbal response 31 from the user.

Alternatively, or in addition the method 100 may further comprisegenerating 151 at least one further verbal statement 50 capable ofprovoking at least one further verbal response 51 from the user. Themethod 100 may further comprise prompting 152 the user to vocallyinteract with the creative work system 200 by vocalizing 153 the atleast one generated further verbal statement 50 to the user via an audiointerface 211 of the creative work system. The method 100 may furthercomprise obtaining 154 at least one further verbal response 51 from theuser via the audio interface. The method 100 may further comprisedetermining 155 at least one contemporaneous feature 40 of the creativework 10 based on the at least one obtained further verbal response 51from the user. In addition, the at least one further verbal response 51from the user may also be used for determining cognitive load 10.

Any response 31 from the user may be used to determine cognitive load 20and/or to determine the contemporaneous feature 40.

A verbal response 31 from the user, a further verbal response 51 fromthe user, and/or a third response 62 (see below) from the user maycomprise e.g. grunting or groaning as such is conducive to determiningcognitive load. A verbal response 31 from the user, a further verbalresponse 51 from the user, and/or a third response 62 (see below) fromthe user may be spoken language comprising a text that is mostly (e.g.apart from grunting or groaning) semantically and/or linguisticallyinterpretable with respect to at least one communication language (e.g.a natural language or an artificial language). Spoken language of theuser is captured via an audio interface 211 of the creative work system200 comprising at least one microphone 212 and at least one speaker 213.

Vocalizing 121 the at least one generated verbal statement 30, the atleast one further generated verbal statement 50, and/or at least onetailored response 60 to the user via the audio interface 211 of thecreative work system 200 may comprise synthesizing at least one audiosignal representing the at least one generated verbal statement 30, theat least one further generated verbal statement 50, and/or at least onetailored response 60. It may further comprise playing the at least oneaudio signal on the audio interface of the creative work system 200.Synthesizing may comprise applying a state-of-the-art algorithm fortext-to-audio conversion.

Rather than or in addition to vocalizing the at least one generated(further) verbal statement via the at least one speaker 213, the atleast one generated verbal statement 30, the at least one furthergenerated verbal statement 50, and/or at least one tailored response 60may also be outputted on a graphical interface 214 of the creative worksystem 200.

The at least one contemporaneous feature 40 of the creative work 10 maycomprise at least one feature (e.g. an apple) of the creative work 10the user of the creative work system 200 has just (e.g. seconds ago)completed, or is currently working on, or is going to work on next. Suchmay depend on the type of question that is asked to identify thecontemporaneous feature. In case of the example in FIG. 4b , thequestion “What are you drawing now?” is likely to be answered with acontemporaneous feature 40 the user is currently working on. Such afeature may also be referred to as a current feature. On the other hand,the term “contemporaneous” may also account for small periods of timesbefore starting to work on a feature or after completing the feature.The at least one feature of the creative work 10 may comprise one ormore of a part of the creative work, an object (e.g. apple) featured bythe creative work, and an aspect of the creative work the user of thecreative work can vocally refer to, name, describe, and/or specify. Ascreation of the creative work 10 progresses, one or more contemporaneousfeatures 40 may be recorded, see FIG. 5 c.

Generating 110 the at least one verbal statement 30 capable of provokingthe at least one verbal response 31 from the user may comprise applyinga candidate verbal statement algorithm configured to generate one ormore candidate verbal statements, e.g. based on one or more candidateprimitives queried from a candidate primitives database 220. A candidateprimitive may be a parametrizable text template. Checking more than onecandidate verbal statements can be used for choosing a (generated)verbal statement that is best suited for cognitive load determination.

Generating 110 the at least one verbal statement 30 capable of provokingthe at least one verbal response 31 from the user may further compriseapplying a conversation algorithm configured to generate for eachcandidate verbal statement one or more predicted verbal responses 32 andcorresponding (i.e. a response probability for each predicted verbalresponse 32) one or more response probabilities, thereby generating, foreach candidate verbal statement, a list of predicted verbal responsesand a vector of response probabilities RPV. The conversation algorithmmay be identical to a common predictive communication algorithm,deployed in voice interfaces or predictive text. In fact, suchsequence-to-sequence models can be trained on entire sequences of text,which in this case will be a set of question sequences, each of whichcorrespond to a set of answer sequences (Q&A language model). Theweighting (or the parameters) used in the machine learn model (e.g. aneural network) can be adjusted to ensure that for a given trainingquestion input, its output is identical (or close to) the actualtraining answer. When the model is then used on a new question it hasnever seen before, it is capable of generalizing, thereby outputting aset of generated answers, each with a confidence value. The confidencevalue in this case can be used as a measure of probability that this isthe “correct” answer, given its training data. The Q&A language modelmay generically be trained on human language (e.g. GTP-3) oradditionally be trained on the language specific users (e.g. voices ofchildren or for particular languages).

Generating 110 the at least one verbal statement 30 capable of provokingthe at least one verbal response 31 from the user may further compriseapplying a predicted verbal response assessment algorithm configured toassign a response score to each predicted verbal response 32, therebygenerating, for each candidate verbal statement, a vector of responsescores RSV. The response score relates to the capability of determiningcognitive load 20: The higher a response score for a predicted verbalresponse 32 is, the more promising the predicted verbal response is fordetermining cognitive load. Hence, the response scores can be used topick the (generated) verbal statement best suited for the determinationof cognitive load 20.

Generating 110 the at least one verbal statement 30 capable of provokingthe at least one verbal response 31 from the user may further comprisee.g. discarding one or more predicted verbal responses 32, if thecorresponding one or more response scores do not satisfy a thresholdcondition (e.g. if a response score is too low). In case of discard, thelist of predicted verbal responses, the vector of response probabilitiesRPV, and/or the vector of response scores RSV need to beadjusted/updated accordingly.

Generating 110 the at least one verbal statement 30 capable of provokingthe at least one verbal response 31 from the user may further compriseapplying a verbal response selection algorithm configured to select oneof the one or more candidate verbal statements based on the one or morevectors of response probabilities RPV and on the one or more vectors ofresponse scores RSV, thereby generating the at least one verbalstatement 30 based on the at least one predicted verbal response 32suitable for determining the cognitive load 20 of the user.

The one or more candidate verbal statements, the one or more predictedverbal responses 32 for each candidate verbal statement, thecorresponding one or more response probabilities, and/or thecorresponding one or more response scores can be stored in a responsedatabase 220. In so doing, such data can later be used to improve thealgorithms, in particular the machine learning algorithms. In the lattercase, such data may be added or incorporated into a larger training dataset machine learning algorithms are trained on. In so doing, thecreative work system 200 can be continuously improved, especially incase of internet connectivity (“internet of things”).

Assigning a response score to a predicted verbal response may comprisesimulatively assessing cognitive load of predicted verbal responses 32.To this end, each predicted verbal response 32 can be synthesized to anaudio signal (that does not necessarily is played on the audiointerface) to be analyzed in terms of cognitive load. Alternatively, orin addition, each predicted verbal response 32 is analyzed in terms ofits semantics (i.e. without synthesizing an audio signal).

Assigning a response score to each predicted verbal response 32 maycomprise checking for one or more verbal features in each predictedverbal response and computing a feature score for each of the one ormore verbal features. A (i.e. any) verbal feature may comprise or be averbal feature of a first type comprising a variety of phoneme use, avariety of pseudo-syllable use, or a response length.

A pseudo-syllable is known to be a syllable-like pattern made up ofmultiple phonemes. Examples of pseudo-syllables may be demonstrated inthe voiced sentence “the dog” which can be split into two standardsyllables (“the” and “dog”) but could also be split into multiplecombinations of voiced (non-standard) pseudo-syllables using the samephonemes such as (“thed” and “og”) or (“th”, “ud”, and “og”). Ingeneral, what pseudo-syllables are may depend on the accent of thespeaker or a speaker's particular emphasis of different vowels andconsonants. Like a normal syllable pseudo-syllables may be constructedby the voiced phonemes corresponding to at least one vowel as well asusually voiced sounds of the consonants on at least one side of the atleast one vowel (or on either side thereof). However, they are referredto as pseudo-syllables as they may not correspond to an acceptedsyllable structure of the given language of interest. In fact,typically, a syllable is considered to have a structure consisting ofonset, nucleus, and coda. A pseudo-syllable may have part of thisstructure but miss other parts or may be composed of non-standardcombinations of voiced consonants and vowels. In applications where onlyassessment of the audio qualities of speech audio is required speech maybe segmented into pseudo-syllables, regardless of whether or not theyconstitute syllables according to the standard structure.

Furthermore, a (i.e. any) verbal feature may comprise or be a verbalfeature of a second type comprising a second type class for a linguisticobject in a sentence, at least one noun, at least one adjective, or atleast one phrase, capable of identifying the at least onecontemporaneous feature 40 of the creative work 10.

Checking for one or more verbal features in each predicted verbalresponse 32 and computing the feature score for each of the one or moreverbal features may comprise applying each predicted verbal response 32to a phoneme use algorithm configured to identify at least one phonemeof the predicted verbal response based on a predetermined list ofphonemes, and to count the phonemes of the predicted verbal response,and to count unique phonemes of the predicted verbal response, and todivide the count of unique phonemes of the predicted verbal response bythe count of the phonemes of the predicted verbal response, therebycomputing the verbal feature score, e.g. a phoneme score. As an example,if every phoneme in the text is the same, the score would be close to 0.If every phoneme in the text is different, the score would be 1.

Furthermore, checking for one or more verbal features in each predictedverbal response 32 and computing the feature score for each of the oneor more verbal features may comprise applying each predicted verbalresponse to a pseudo-syllable use algorithm configured to identify atleast one pseudo-syllable of the predicted verbal response based on aset of at least one predetermined rule (e.g. any string of charactersconsisting of one or more vowels and a consonant), and to count thepseudo-syllables of the predicted verbal response, and to count theunique pseudo-syllables of the predicted verbal response, and to dividethe count of unique pseudo-syllables by the count of the uniquepseudo-syllables, thereby computing the verbal feature score, e.g. apseudo-syllable score.

Furthermore, checking for one or more verbal features in each predictedverbal response 32 and computing the feature score for each of the oneor more verbal features may comprise applying each predicted verbalresponse to a response length algorithm configured to identify at leastone word of the predicted verbal response, and to count the words, andto compute the verbal feature score, e.g. a response length score, basedon a comparison of the count of the words to a predetermined referencevalue. An ideal predetermined reference value can be a response lengththat is long enough that any increase in length would not appreciablyadd value in terms of instances of new vocal features. As an example, asentence with 15 words may have far more utility than a sentence withjust two words. On the other hand, a sentence with 100 words may only bemarginally more useful than one with 50 words. Realistically, thislength would have to be determined experimentally, and may also includeadjustments for considerations based on user-friendliness (e.g. a 30word response may be ideal, but if only a fraction of users actuallyrespond with this many words it would not be a useful reference value).

Furthermore, checking for one or more verbal features in each predictedverbal response 32 and computing the feature score for each of the oneor more verbal features may comprise applying each predicted verbalresponse to a language algorithm configured to identify the verbalfeatures of a second type class of the predicted verbal response, and tocount words of the verbal features of the second type class of thepredicted verbal response, and to count words of the predicted verbalresponse, and to divide the count of words of the verbal features of thesecond type class of the predicted verbal response by the count of thewords of the predicted verbal response, thereby computing the verbalfeature score.

Assigning a response score to each predicted verbal response 32 may bebased on at least one verbal feature score corresponding to at least oneverbal feature of the predicted verbal response. The response score toeach predicted verbal response 32 can be computed as an average of theverbal feature scores corresponding to the one or more verbal featuresof the predicted verbal response.

Selecting one of the one or more candidate verbal statements based onthe one or more vectors of response probabilities RPV and on the one ormore vectors of response scores RSV may comprise multiplying (i.e.component-wise), for each candidate verbal statement, the vector ofresponse probabilities RPV and the vector of response scores RSV,thereby generating, for each candidate verbal statement, a vector ofweighted response scores

WRSV=RPV.*RSV

and summing, for each candidate verbal statement, components of thevector of weighted response scores WRSV, thereby generating, for eachcandidate verbal statement, a total selection score, and selecting oneof the one or more candidate verbal statements with the highest totalselection score.

Obtaining 130 the at least one verbal response 31 from the user via theaudio interface 211 may comprise obtaining the at least one verbalresponse from the user in terms of a timestamped audio waveform (e.g. interms of an audio waveform and a timestamp marking a starting point ofthe audio waveform).

Determining 140 the cognitive load 20 of the user based on the at leastone verbal response 31 obtained from the user may comprise assessing atleast one vocal feature of the at least one verbal response obtainedfrom the user. A vocal feature may be a verbal feature of the first typeor a change in pitch with respect to time, or a periodicity or avariation in low-frequency glottal pulses.

Determining 140 the cognitive load 20 of the user based on the at leastone verbal response 31 obtained from the user may comprise applying oneor more cognitive load feature assessment algorithms (along the lines ofQuatieri et al.), wherein each cognitive load feature assessmentalgorithm corresponds to a vocal feature and is configured to generate avocal feature vector representation of the corresponding vocal featurefor the at least one verbal response 31 from the user, therebygenerating one or more vocal feature vector representations.

Determining 140 the cognitive load 20 of the user based on the at leastone verbal response 31 obtained from the user may comprises applying theone or more vocal feature vector representations to a cognitive loadscore algorithm configured to compare each of the one or more vocalfeature vector representations to at least one predetermined benchmarkfor cognitive load, wherein each comparison comprises computing apredetermined criterion (e.g. each vocal feature vector representationmay have a predetermined criterion of its own.) based on at least onevocal feature vector representation and the at least one predeterminedbenchmark for cognitive load, and to count the one or more vocal featurevector representations satisfying the corresponding predeterminedcriteria, and to count the one or more vocal feature vectorrepresentations, and to divide a count of the one or more vocal featurevector representations satisfying the corresponding predeterminedcriteria by a count of the one or more vocal feature vectorrepresentations, thereby determining the cognitive load score of theuser, thereby determining the cognitive load 20 of the user. Thecognitive load score may e.g. be a real number in the interval [0, 1](including the boundaries). Note that vocal features may have differentpredetermined benchmarks for cognitive load. The predetermined benchmarkfor cognitive load may vary depending on the vector representation ofthe vocal feature. A simple case would be a set of “benchmark” vectors,one for each feature type, which can be used for directelement-by-element comparison with the measured feature vectors.Furthermore, as an example, it is possible to compare an eigenvaluespectrum of a set of “high cognitive load” feature vectors to aneigenvalue spectrum of “low cognitive load” feature vectors. In thatcase, the benchmark may be an eigenvalue spectrum which represents thethreshold between low and high cognitive load.

The cognitive load score may inherit a timestamp of the timestampedaudio waveform. The cognitive load score and the corresponding timestampmay be stored in a score database 220, 221.

Generating 151 the at least one further verbal statement 50 capable ofprovoking at least one further verbal response 51 from the user maycomprise selecting a question about the at least one contemporaneousfeature 40. Some generated verbal statements may have been taggedinitially as “question statements”. They can be questions which willencourage the user to respond with a response that contains informationon the contemporaneous feature 40 of the creative work. They need not bespecific to any particular feature but can be general questions, alongthe lines of e.g. “what are you drawing now?” or “what are you workingon now?”, see FIG. 4b . The audio interface may vocalize these questionstatements each time it determines the cognitive load of a user, and theuser gives some response which is analyzed. Therefore, it can be saidthat the user response used for determining cognitive load iscontemporaneous to the user response used for determining the createdfeature, i.e. the determined cognitive load is related to that createdfeature, i.e. the contemporaneous feature 40. Because the receipt ofeach user response can be timestamped, these timestamps can be used toidentify the contemporaneous cognitive loads and the created(contemporaneous) features.

Obtaining 154 the at least one further verbal response 51 from the uservia the audio interface 211 may comprise obtaining the at least onefurther verbal response from the user in terms of a further timestampedaudio waveform (e.g. in terms of a further audio waveform and a furthertimestamp marking a starting point of the further audio waveform).

The at least one further verbal response 51 from the user can be appliedto a speech-to-text algorithm, thereby generating a verbal featurestring inheriting a further timestamp from the further timestamped audiowaveform. The generated verbal feature string may be used for the recordor report after completion of the creative work 10, cf. FIG. 5c .Alternatively, or in addition, the at least one verbal response 31 fromthe user is applied to a speech-to-text algorithm, thereby generating averbal feature string inheriting a further timestamp from thetimestamped audio waveform.

Determining 155 the at least one contemporaneous feature 40 of thecreative work 10 based on the obtained at least one verbal response 31from the user or on the obtained at least one further verbal response 51from the user may comprise applying the verbal feature string to afeature recognition algorithm configured to identify a noun that mostlikely relates to the at least one contemporaneous feature of thecreative work.

Identifying the at least one contemporaneous feature 40 of the creativework 10 may comprise applying a text split algorithm configured to breakthe verbal feature string into one or more subunits each comprisingsentences, phrases, clauses, and/or words, and applying a nounextraction algorithm configured to perform a look-up for one or moresubunits in a generic dictionary providing the at least one noun for theat least one contemporaneous feature of the creative work, and e.g.,applying a noun selection algorithm configured to select a noun thatmost likely relates to the at least one contemporaneous feature 40 ofthe creative work 10 based on one or more provided nouns, therebyidentifying the noun that most likely relates to the at least onecontemporaneous feature of the creative work. As an example, see FIG. 4b, “apple” may be extracted and recognized from “I am drawing a big redapple.”.

The feature recognition algorithm, e.g. the text split algorithm, thenoun extraction algorithm and/or the noun selection algorithm, maycomprise at least one pre-trained machine learning algorithm, e.g.providing more than one answers with corresponding probabilities.

The noun that most likely relates to the at least one contemporaneousfeature 40 of the creative work 10 may inherit the further timestamp ofthe verbal feature string. The at least one contemporaneous feature 40of the creative work 10, the noun that most likely relates to the atleast one contemporaneous feature of the creative work and the furthertimestamp, and e.g. the verbal feature string may be stored in the scoredatabase 220, 221.

In an embodiment, the method 100 may further comprise generating 160 atleast one tailored response 60 based on the cognitive load 20 and/or thecorresponding contemporaneous feature 40, wherein, as an example, the atleast one tailored response aims at influencing the user of the creativework system 200, see FIG. 5a-c . The at least one tailored response 60may comprise psychological means to engage the user with the creativework 10. Such psychological means may comprise encouragement and/orpraise. Furthermore, generating 160 the at least one tailored response60 may be triggered by a request of a user or of a further individual(e.g. a supervisor or teacher), or automatically when the cognitive load20 exceeds a predetermined cognitive load threshold. Furthermore

In an embodiment, the method 100 may further comprising generating 161at least one further tailored response 60 based on at least onecognitive load 20 and/or the at least one corresponding contemporaneousfeature 40 queried from the score database 220, 221. In other words, theat least one further tailored response 60 may refer to one or morecontemporaneous features of the past. This can be used to generate andoutput a record or a report e.g. after completion of the creative workand covering a period of time in the past. Querying at least onecognitive load 20 and/or the at least one corresponding contemporaneousfeature 40 can be subject to one or more conditions comprising acondition restricting stored timestamps, and a condition restrictingstored cognitive loads, and a condition restricting storedcontemporaneous features. Such conditions may be set via the userinterface 210. As an example, a condition can be “all cognitive load(scores) above a threshold and within the past five minutes”.

The at least one tailored response 60 can be based on a tailoredresponse template 61, see e.g. FIG. 5a-c , e.g. queried from a tailoredresponse template database 220. Other parameters such as e.g. a name ofthe user may be used in this retrieval as well. The tailored responsetemplate 61 may be a parametrizable text. Furthermore, generating 160,161 the at least one tailored response 60 based on the cognitive load 20and the corresponding contemporaneous feature 40 may comprise applying atailored response algorithm configured to integrate at least onecognitive load, the at least one corresponding contemporaneous feature,and/or at least one corresponding timestamp into the tailored responsetemplate 61. A verbal statement, and/or e.g. a tailored response maycomprise a name of the user.

The method 100 may comprise outputting 170 the at least one tailoredresponse via a user interface 210 to the user, e.g. on a graphicalinterface 214 and/or via the audio interface 211.

The at least one generated tailored response 60 can be capable ofprovoking a third response (e.g. “yes” or “no”) 62 from the user, andthe method further may comprise prompting the user to vocally interactwith the creative work system 200 by vocalizing the at least onegenerated tailored response 60, and obtaining the third response 62 fromthe user via the user interface 210 (e.g. a button of the user interface210 or the audio interface 211), e.g. the audio interface 211, andexecuting a third response algorithm based on the third response 62 fromthe user, e.g. wherein the third response algorithm is configured tofeed back 180 information related to the at least one cognitive load 20and/or the at least one corresponding contemporaneous feature 40 via theuser interface 210, if the third response 62 from the user is recognizedto be affirmative. In other words, feeding back 180 such information maybe carried out if the third response algorithm recognizes a “yes” in thethird response 62 from the user. The information related to the at leastone cognitive load 20 and/or the at least one correspondingcontemporaneous feature 40 may contribute to creating the creative work10. As an example, the information may comprise an image or a videotutorial on how to draw an object (e.g. an apple), see FIG. 5c , displayon the graphical interface 214.

FIG. 2a-b schematically illustrate embodiments of thecomputer-implemented method 100 according to the first aspect formeasuring cognitive load 20 of the user creating the creative work 10 ina creative work system 200. In FIG. 2a , both the cognitive load 20 ofthe user and the contemporaneous feature 40 are determined based on theverbal response 31 from the user. On the other hand, in FIG. 2b , thecognitive load 20 of the user is determined based on the verbal response31 from the user and the contemporaneous feature 40 is determined basedon the further verbal response 51 from the user.

The creative work system 200 for measuring cognitive load 20 of a usercreating the creative work 10 may comprise a user interface 210comprising an audio interface 211. The creative work system may beconfigured to run the method 100 of the first aspect (or an embodimentthereof). FIG. 3 schematically illustrates the creative work system 200.

The audio interface 211 may comprise at least one microphone 212 and atleast one speaker 213 (i.e. a loudspeaker).

The creative work system 200 may comprise access to at least onedatabase 220, e.g. to the score database 221, via a communicationchannel 222. A database 220, or the score database 221, may either bepart of the creative work system 200 or accessed via a communicationchannel 222.

The creative work system 200 may comprise a camera 230 configured torecord at least one image or a sequence of images of the creative work10 as creation progresses. This can be used to determine cognitive loadalso by analyzing a captured video sequence (e.g. based on eye tracking)of the user. In so doing, the accuracy of the determination of the(overall) cognitive load can be improved (e.g. by averaging overauditive and visual cognitive load scores). Alternatively, or inaddition, a visual object-recognition algorithm (e.g. a pre-trainedimage recognition model) may be applied that is configured to identifyone or more visual features of the creative work 10 and to correlatethem to cognitive load (e.g. via timestamp matching).

The user interface 210 may comprise a graphical interface 214. This canbe used to display information such as images or videos.

One or more implementations disclosed herein include and/or may beimplemented using a machine learning model. For example, one or more ofthe response algorithm, candidate verbal statement algorithm,conversation algorithm, common predictive communication algorithm,predicted verbal response assessment algorithm, verbal responseselection algorithm, machine learning algorithms, phoneme use algorithm,pseudo-syllable use algorithm, response length algorithm, languagealgorithm, cognitive load feature assessment algorithm, cognitive loadscore algorithm, speech-to-text algorithm, feature recognitionalgorithm, text split algorithm, noun extraction algorithm, nounselection algorithm, pre-trained machine learning algorithm and/orvisual object-recognition algorithm may be implemented using a machinelearning model and/or may be used to train a machine learning model. Agiven machine learning model may be trained using the data flow 610 ofFIG. 6. Training data 612 may include one or more of stage inputs 614and known outcomes 618 related to a machine learning model to betrained. The stage inputs 614 may be from any applicable sourceincluding text, visual representations, data, values, comparisons, stageoutputs (e.g., one or more outputs from a step from FIGS. 1a, 1b, 2a, 2b, and/or 3). The known outcomes 618 may be included for machine learningmodels generated based on supervised or semi-supervised training. Anunsupervised machine learning model may not be trained using knownoutcomes 618. Known outcomes 618 may include known or desired outputsfor future inputs similar to or in the same category as stage inputs 614that do not have corresponding known outputs.

The training data 612 and a training algorithm 620 (e.g., responsealgorithm, candidate verbal statement algorithm, conversation algorithm,common predictive communication algorithm, predicted verbal responseassessment algorithm, verbal response selection algorithm, machinelearning algorithms, phoneme use algorithm, pseudo-syllable usealgorithm, response length algorithm, language algorithm, cognitive loadfeature assessment algorithm, cognitive load score algorithm,speech-to-text algorithm, feature recognition algorithm, text splitalgorithm, noun extraction algorithm, noun selection algorithm,pre-trained machine learning algorithm and/or visual object-recognitionalgorithm implemented using a machine learning model and/or may be usedto train a machine learning model) may be provided to a trainingcomponent 630 that may apply the training data 612 to the trainingalgorithm 620 to generate a machine learning model. According to animplementation, the training component 630 may be provided comparisonresults 616 that compare a previous output of the corresponding machinelearning model to apply the previous result to re-train the machinelearning model. The comparison results 616 may be used by the trainingcomponent 630 to update the corresponding machine learning model. Thetraining algorithm 620 may utilize machine learning networks and/ormodels including, but not limited to a deep learning network such asDeep Neural Networks (DNN), Convolutional Neural Networks (CNN), FullyConvolutional Networks (FCN) and Recurrent Neural Networks (RCN),probabilistic models such as Bayesian Networks and Graphical Models,and/or discriminative models such as Decision Forests and maximum marginmethods, or the like.

A machine learning model used herein may be trained and/or used byadjusting one or more weights and/or one or more layers of the machinelearning model. For example, during training, a given weight may beadjusted (e.g., increased, decreased, removed) based on training data orinput data. Similarly, a layer may be updated, added, or removed basedon training data/and or input data. The resulting outputs may beadjusted based on the adjusted weights and/or layers.

In general, any process or operation discussed in this disclosure thatis understood to be computer-implementable, such as the processillustrated in FIGS. 1a, 1b, 2a, 2b , and/or 3 may be performed by oneor more processors of a computer system as described above. A process orprocess step performed by one or more processors may also be referred toas an operation. The one or more processors may be configured to performsuch processes by having access to instructions (e.g., software orcomputer-readable code) that, when executed by the one or moreprocessors, cause the one or more processors to perform the processes.The instructions may be stored in a memory of the computer system. Aprocessor may be a central processing unit (CPU), a graphics processingunit (GPU), or any suitable types of processing unit.

A computer system, such as a system or device implementing a process oroperation in the examples above, may include one or more computingdevices. One or more processors of a computer system may be included ina single computing device or distributed among a plurality of computingdevices. One or more processors of a computer system may be connected toa data storage device. A memory of the computer system may include therespective memory of each computing device of the plurality of computingdevices.

In various embodiments, one or more portions of method 100 and system200 may be implemented in, for instance, a chip set including aprocessor and a memory as shown in FIG. 7. FIG. 7 illustrates animplementation of a general computer system that may execute techniquespresented herein. The computer system 700 can include a set ofinstructions that can be executed to cause the computer system 700 toperform any one or more of the methods or computer based functionsdisclosed herein. The computer system 700 may operate as a standalonedevice or may be connected, e.g., using a network, to other computersystems or peripheral devices.

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specification,discussions utilizing terms such as “processing,” “computing,”“determining”, “analyzing” or the like, refer to the action and/orprocesses of a computer or computing system, or similar electroniccomputing device, that manipulate and/or transform data represented asphysical, such as electronic, quantities into other data similarlyrepresented as physical quantities.

In a similar manner, the term “processor” may refer to any device orportion of a device that processes electronic data, e.g., from registersand/or memory to transform that electronic data into other electronicdata that, e.g., may be stored in registers and/or memory. A “computer,”a “computing machine,” a “computing platform,” a “computing device,” ora “server” may include one or more processors.

In a networked deployment, the computer system 700 may operate in thecapacity of a server or as a client user computer in a server-clientuser network environment, or as a peer computer system in a peer-to-peer(or distributed) network environment. The computer system 700 can alsobe implemented as or incorporated into various devices, such as apersonal computer (PC), a tablet PC, a personal digital assistant (PDA),a mobile device, a palmtop computer, a laptop computer, a desktopcomputer, a communications device, a wireless telephone, a land-linetelephone, a control system, a camera, a scanner, a facsimile machine, apersonal trusted device, a web appliance, a network router, switch orbridge, or any other machine capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatmachine. In a particular implementation, the computer system 700 can beimplemented using electronic devices that provide voice, video, or datacommunication. Further, while a computer system 700 is illustrated as asingle system, the term “system” shall also be taken to include anycollection of systems or sub-systems that individually or jointlyexecute a set, or multiple sets, of instructions to perform one or morecomputer functions.

As illustrated in FIG. 7, the computer system 700 may include aprocessor 702, e.g., a central processing unit (CPU), a graphicsprocessing unit (GPU), or both. The processor 702 may be a component ina variety of systems. For example, the processor 702 may be part of astandard personal computer or a workstation. The processor 702 may beone or more general processors, digital signal processors, applicationspecific integrated circuits, field programmable gate arrays, servers,networks, digital circuits, analog circuits, combinations thereof, orother now known or later developed devices for analyzing and processingdata. The processor 702 may implement a software program, such as codegenerated manually (i.e., programmed).

The computer system 700 may include a memory 704 that can communicatevia a bus 708. The memory 704 may be a main memory, a static memory, ora dynamic memory. The memory 704 may include, but is not limited tocomputer readable storage media such as various types of volatile andnon-volatile storage media, including but not limited to random accessmemory, read-only memory, programmable read-only memory, electricallyprogrammable read-only memory, electrically erasable read-only memory,flash memory, magnetic tape or disk, optical media and the like. In oneimplementation, the memory 704 includes a cache or random-access memoryfor the processor 702. In alternative implementations, the memory 704 isseparate from the processor 702, such as a cache memory of a processor,the system memory, or other memory. The memory 704 may be an externalstorage device or database for storing data. Examples include a harddrive, compact disc (“CD”), digital video disc (“DVD”), memory card,memory stick, floppy disc, universal serial bus (“USB”) memory device,or any other device operative to store data. The memory 704 is operableto store instructions executable by the processor 702. The functions,acts or tasks illustrated in the figures or described herein may beperformed by the processor 702 executing the instructions stored in thememory 704. The functions, acts or tasks are independent of theparticular type of instructions set, storage media, processor orprocessing strategy and may be performed by software, hardware,integrated circuits, firm-ware, micro-code and the like, operating aloneor in combination. Likewise, processing strategies may includemultiprocessing, multitasking, parallel processing and the like.

As shown, the computer system 700 may further include a display 710,such as a liquid crystal display (LCD), an organic light emitting diode(OLED), a flat panel display, a solid-state display, a cathode ray tube(CRT), a projector, a printer or other now known or later developeddisplay device for outputting determined information. The display 710may act as an interface for the user to see the functioning of theprocessor 702, or specifically as an interface with the software storedin the memory 704 or in the drive unit 706.

Additionally or alternatively, the computer system 700 may include aninput/output device 712 configured to allow a user to interact with anyof the components of computer system 700. The input/output device 712may be a number pad, a keyboard, or a cursor control device, such as amouse, or a joystick, touch screen display, remote control, or any otherdevice operative to interact with the computer system 700.

The computer system 700 may also or alternatively include drive unit 706implemented as a disk or optical drive. The drive unit 706 may include acomputer-readable medium 722 in which one or more sets of instructions724, e.g. software, can be embedded. Further, instructions 724 mayembody one or more of the methods or logic as described herein. Theinstructions 724 may reside completely or partially within the memory704 and/or within the processor 702 during execution by the computersystem 700. The memory 704 and the processor 702 also may includecomputer-readable media as discussed above.

In some systems, a computer-readable medium 722 includes instructions724 or receives and executes instructions 724 responsive to a propagatedsignal so that a device connected to a network 770 can communicatevoice, video, audio, images, or any other data over the network 770.Further, the instructions 724 may be transmitted or received over thenetwork 770 via a communication port or interface 720, and/or using abus 708. The communication port or interface 720 may be a part of theprocessor 702 or may be a separate component. The communication port orinterface 720 may be created in software or may be a physical connectionin hardware. The communication port or interface 720 may be configuredto connect with a network 770, external media, the display 710, or anyother components in computer system 700, or combinations thereof. Theconnection with the network 770 may be a physical connection, such as awired Ethernet connection or may be established wirelessly as discussedbelow. Likewise, the additional connections with other components of thecomputer system 700 may be physical connections or may be establishedwirelessly. The network 770 may alternatively be directly connected to abus 708.

While the computer-readable medium 722 is shown to be a single medium,the term “computer-readable medium” may include a single medium ormultiple media, such as a centralized or distributed database, and/orassociated caches and servers that store one or more sets ofinstructions. The term “computer-readable medium” may also include anymedium that is capable of storing, encoding, or carrying a set ofinstructions for execution by a processor or that cause a computersystem to perform any one or more of the methods or operations disclosedherein. The computer-readable medium 722 may be non-transitory, and maybe tangible.

The computer-readable medium 722 can include a solid-state memory suchas a memory card or other package that houses one or more non-volatileread-only memories. The computer-readable medium 722 can be arandom-access memory or other volatile re-writable memory. Additionallyor alternatively, the computer-readable medium 722 can include amagneto-optical or optical medium, such as a disk or tapes or otherstorage device to capture carrier wave signals such as a signalcommunicated over a transmission medium. A digital file attachment to ane-mail or other self-contained information archive or set of archivesmay be considered a distribution medium that is a tangible storagemedium. Accordingly, the disclosure is considered to include any one ormore of a computer-readable medium or a distribution medium and otherequivalents and successor media, in which data or instructions may bestored.

In an alternative implementation, dedicated hardware implementations,such as application specific integrated circuits, programmable logicarrays and other hardware devices, can be constructed to implement oneor more of the methods described herein. Applications that may includethe apparatus and systems of various implementations can broadly includea variety of electronic and computer systems. One or moreimplementations described herein may implement functions using two ormore specific interconnected hardware modules or devices with relatedcontrol and data signals that can be communicated between and throughthe modules, or as portions of an application-specific integratedcircuit. Accordingly, the present system encompasses software, firmware,and hardware implementations.

The computer system 700 may be connected to a network 770. The network770 may define one or more networks including wired or wirelessnetworks. The wireless network may be a cellular telephone network, an802.11, 802.16, 802.20, or WiMAX network. Further, such networks mayinclude a public network, such as the Internet, a private network, suchas an intranet, or combinations thereof, and may utilize a variety ofnetworking protocols now available or later developed including, but notlimited to TCP/IP based networking protocols. The network 770 mayinclude wide area networks (WAN), such as the Internet, local areanetworks (LAN), campus area networks, metropolitan area networks, adirect connection such as through a Universal Serial Bus (USB) port, orany other networks that may allow for data communication. The network770 may be configured to couple one computing device to anothercomputing device to enable communication of data between the devices.The network 770 may generally be enabled to employ any form ofmachine-readable media for communicating information from one device toanother. The network 770 may include communication methods by whichinformation may travel between computing devices. The network 770 may bedivided into sub-networks. The sub-networks may allow access to all ofthe other components connected thereto or the sub-networks may restrictaccess between the components. The network 770 may be regarded as apublic or private network connection and may include, for example, avirtual private network or an encryption or other security mechanismemployed over the public Internet, or the like.

In accordance with various implementations of the present disclosure,the methods described herein may be implemented by software programsexecutable by a computer system. Further, in an exemplary, non-limitedimplementation, implementations can include distributed processing,component/object distributed processing, and parallel processing.Alternatively, virtual computer system processing can be constructed toimplement one or more of the methods or functionality as describedherein.

Although the present invention has been described above and is definedin the attached claims, it should be understood that the invention mayalternatively be defined in accordance with the following embodiments:

-   1. A computer-implemented method (100) for measuring cognitive load    (20) of a user creating a creative work (10) in a creative work    system (200), comprising:    -   generating (110) at least one verbal statement (30) capable of        provoking at least one verbal response (31) from the user; and    -   prompting (120) the user to vocally interact with the creative        work system by vocalizing (121) the at least one generated        verbal statement (30) to the user via an audio interface (211)        of the creative work system; and    -   obtaining (130) the at least one verbal response (31) from the        user via the audio interface; and    -   determining (140) the cognitive load (20) of the user based on        the at least one verbal response (31) obtained from the user;    -   wherein generating (110) the at least one verbal statement (30)        is based on at least one predicted verbal response (32) suitable        for determining the cognitive load (20) of the user.-   2. The method (100) of embodiment 1, further comprising:    -   determining (150) at least one contemporaneous feature (40) of        the creative work (10) based on the at least one obtained verbal        response (31) from the user.-   3. The method (100) of embodiment 1, further comprising:    -   generating (151) at least one further verbal statement (50)        capable of provoking at least one further verbal response (51)        from the user; and    -   prompting (152) the user to vocally interact with the creative        work system (200) by vocalizing (153) the at least one generated        further verbal statement (50) to the user via an audio interface        (211) of the creative work system; and    -   obtaining (154) at least one further verbal response (51) from        the user via the audio interface; and    -   determining (155) at least one contemporaneous feature (40) of        the creative work (10) based on the at least one obtained        further verbal response (51) from the user.-   4. The method (100) of one of the preceding embodiments, wherein the    creative work (10) comprises a visual artwork.-   5. The method (100) of embodiment 4, wherein the visual artwork    comprises a drawing, a painting, calligraphy and/or a sculpture.-   6. The method (100) of one of the preceding embodiments, wherein the    creative work (10) comprises writing.-   7. The method (100) of one of the preceding embodiments, wherein the    at least one verbal response (31) from the user is spoken language.-   8. The method (100) of one of the preceding embodiments, when    dependent on embodiment 3, wherein the at least one further verbal    response (51) from the user is spoken language.-   9. The method (100) of one of the preceding embodiments, wherein the    audio interface (211) of the creative work system (200) comprises at    least one microphone (212) and at least one speaker (213).-   10. The method (100) of one of the preceding embodiments, wherein    vocalizing (121) the at least one generated verbal statement (30) to    the user via the audio interface (211) of the creative work system    (200) comprises:    -   synthesizing at least one audio signal representing the at least        one generated verbal statement; and    -   playing the at least one audio signal on the audio interface of        the creative work system.-   11. The method (100) of one of the preceding embodiments, when    dependent on embodiment 3, wherein vocalizing (153) the at least one    further generated verbal statement (50) to the user via the audio    interface (211) of the creative work system (200) comprises:    -   synthesizing at least one further audio signal representing the        at least one further generated verbal statement; and    -   playing the at least one further audio signal on the audio        interface of the creative work system.-   12. The method (100) of one of the preceding embodiments, when    dependent on embodiment 2 or 3, wherein the at least one    contemporaneous feature (40) of the creative work (10) comprises at    least one feature of the creative work the user of the creative work    system (200):    -   has just completed; or    -   is currently working on; or    -   is going to work on next.-   13. The method (100) of embodiment 12, wherein the at least one    feature of the creative work (10) comprises one or more of:    -   a part of the creative work; and    -   an object featured by the creative work; and    -   an aspect of the creative work the user of the creative work can        vocally refer to, name, describe, and/or specify.-   14. The method (100) of one of the preceding embodiments, wherein    generating (110) the at least one verbal statement (30) capable of    provoking the at least one verbal response (31) from the user    comprises:    -   applying a candidate verbal statement algorithm configured to        generate one or more candidate verbal statements; and    -   applying a conversation algorithm configured to generate for        each candidate verbal statement one or more predicted verbal        responses (32) and corresponding one or more response        probabilities, thereby generating, for each candidate verbal        statement, a list of predicted verbal responses and a vector of        response probabilities RPV; and    -   applying a predicted verbal response assessment algorithm        configured to assign a response score to each predicted verbal        response (32), thereby generating, for each candidate verbal        statement, a vector of response scores; and applying a verbal        response selection algorithm configured to select one of the one        or more candidate verbal statements based on the one or more        vectors of response probabilities and on the one or more vectors        of response scores, thereby generating the at least one verbal        statement (30) based on the at least one predicted verbal        response (32) suitable for determining the cognitive load (20)        of the user.-   15. The method (100) of embodiment 14, wherein the one or more    candidate verbal statements, the one or more predicted verbal    responses (32) for each candidate verbal statement, the    corresponding one or more response probabilities, and/or the    corresponding one or more response scores are stored in a response    database (220).-   16. The method (100) of embodiment 14 or 15, wherein assigning a    response score to each predicted verbal response (32) comprises    checking for one or more verbal features in each predicted verbal    response and computing a feature score for each of the one or more    verbal features.-   17. The method (100) of embodiment 16, wherein a verbal feature    comprises a verbal feature of a first type comprising:    -   a variety of phoneme use; or    -   a variety of pseudo-syllable use; or    -   a response length.-   18. The method (100) of embodiment 16 or 17, when dependent on    embodiment 2 or 3, wherein a verbal feature comprises a verbal    feature of a second type comprising a second type class for:    -   a linguistic object in a sentence; or    -   at least one noun; or    -   at least one adjective; or    -   at least one phrase;    -   capable of identifying the at least one contemporaneous feature        of the creative work (10).-   19. The method (100) of one of the embodiments 16 to 18, when    dependent on embodiment 17, wherein checking for one or more verbal    features in each predicted verbal response (32) and computing the    feature score for each of the one or more verbal features comprises    applying each predicted verbal response (32) to a phoneme use    algorithm configured:    -   to identify at least one phoneme of the predicted verbal        response based on a predetermined list of phonemes; and    -   to count the phonemes of the predicted verbal response; and    -   to count unique phonemes of the predicted verbal response; and    -   to divide the count of unique phonemes of the predicted verbal        response by the count of the phonemes of the predicted verbal        response, thereby computing a phoneme score, thereby computing        the verbal feature score.-   20. The method (100) of one of the embodiments 16 to 19, when    dependent on embodiment 17, wherein checking for one or more verbal    features in each predicted verbal response (32) and computing the    feature score for each of the one or more verbal features comprises    applying each predicted verbal response to a pseudo-syllable use    algorithm configured:    -   to identify at least one pseudo-syllable of the predicted verbal        response based on a set of at least one predetermined rule; and    -   to count the pseudo-syllables of the predicted verbal response;        and    -   to count the unique pseudo-syllables of the predicted verbal        response; and    -   to divide the count of unique pseudo-syllables by the count of        the unique pseudo-syllables, thereby computing a pseudo-syllable        score, thereby computing the verbal feature score.-   21. The method (100) of one of the embodiments 16 to 20, when    dependent on embodiment 17, wherein checking for one or more verbal    features in each predicted verbal response (32) and computing the    feature score for each of the one or more verbal features comprises    applying each predicted verbal response to a response length    algorithm configured:    -   to identify at least one word of the predicted verbal response;        and    -   to count the words; and    -   to compute a response length score, thereby computing the verbal        feature score, based on a comparison of the count of the words        to a predetermined reference value.-   22. The method (100) of one of the embodiments 16 to 21, when    dependent on embodiment 18, wherein checking for one or more verbal    features in each predicted verbal response (32) and computing the    feature score for each of the one or more verbal features comprises    applying each predicted verbal response to a language algorithm    configured:    -   to identify the verbal features of a second type class of the        predicted verbal response; and    -   to count words of the verbal features of the second type class        of the predicted verbal response; and    -   to count words of the predicted verbal response; and    -   to divide the count of words of the verbal features of the        second type class of the predicted verbal response by the count        of the words of the predicted verbal response, thereby computing        the verbal feature score.-   23. The method (100) of one of the embodiments 16 to 22, wherein    assigning a response score to each predicted verbal response (32) is    based on at least one verbal feature score corresponding to at least    one verbal feature of the predicted verbal response.-   24. The method (100) of embodiment 23, wherein the response score to    each predicted verbal response (32) is computed as an average of the    verbal feature scores corresponding to the one or more verbal    features of the predicted verbal response.-   25. The method (100) of one of the embodiments 14 to 24, wherein    selecting one of the one or more candidate verbal statements based    on the one or more vectors of response probabilities and on the one    or more vectors of response scores comprises:    -   multiplying, for each candidate verbal statement, the vector of        response probabilities RPV and the vector of response scores,        thereby generating, for each candidate verbal statement, a        vector of weighted response scores; and    -   summing, for each candidate verbal statement, components of the        vector of weighted response scores, thereby generating, for each        candidate verbal statement, a total selection score; and    -   selecting one of the one or more candidate verbal statements        with the highest total selection score.-   26. The method (100) of one of the preceding embodiments, wherein    obtaining (130) the at least one verbal response (31) from the user    via the audio interface (211) comprises obtaining the at least one    verbal response from the user in terms of a timestamped audio    waveform.-   27. The method (100) of one of the preceding embodiments, wherein    determining (140) the cognitive load (20) of the user based on the    at least one verbal response (31) obtained from the user comprises    assessing at least one vocal feature of the at least one verbal    response obtained from the user.-   28. The method (100) of embodiment 27, when dependent on embodiment    17, wherein a vocal feature is a verbal feature of the first type    or:    -   a change in pitch    -   a periodicity or a variation in low-frequency glottal pulses.-   29. The method (100) of embodiment 27 or 28, wherein determining    (140) the cognitive load (20) of the user based on the at least one    verbal response (31) obtained from the user comprises applying one    or more cognitive load feature assessment algorithms, wherein each    cognitive load feature assessment algorithm corresponds to a vocal    feature and is configured:    -   to generate a vocal feature vector representation of the        corresponding vocal feature for the at least one verbal response        (31) from the user,    -   thereby generating one or more vocal feature vector        representations.-   30. The method (100) of embodiment 29, wherein determining (140) the    cognitive load (20) of the user based on the at least one verbal    response (31) obtained from the user comprises applying the one or    more vocal feature vector representations to a cognitive load score    algorithm configured:    -   to compare each of the one or more vocal feature vector        representations to at least one predetermined benchmark for        cognitive load, wherein each comparison comprises computing a        predetermined criterion based on at least one vocal feature        vector representation and the at least one predetermined        benchmark for cognitive load; and    -   to count the one or more vocal feature vector representations        satisfying the corresponding predetermined criteria; and    -   to count the one or more vocal feature vector representations;        and    -   to divide a count of the one or more vocal feature vector        representations satisfying the corresponding predetermined        criteria by a count of the one or more vocal feature vector        representations, thereby determining the cognitive load score of        the user, thereby determining the cognitive load (20) of the        user.-   31. The method (100) of one of the preceding embodiments, when    dependent on embodiment 26, wherein the cognitive load score    inherits a timestamp of the timestamped audio waveform.-   32. The method (100) of embodiment 31, wherein cognitive load score    and the corresponding timestamp are stored in a score database (220,    221).-   33. The method (100) of one of the preceding embodiments, when    dependent on embodiment 3, wherein generating (151) the at least one    further verbal statement (50) capable of provoking at least one    further verbal response (51) from the user comprises selecting a    question about the at least one contemporaneous feature (40).-   34. The method (100) of one of the preceding embodiments, when    dependent on embodiment 3, wherein obtaining (154) the at least one    further verbal response (51) from the user via the audio interface    (211) comprises obtaining the at least one further verbal response    from the user in terms of a further timestamped audio waveform.-   35. The method (100) of embodiment 34, wherein the at least one    further verbal response from the user is applied to a speech-to-text    algorithm, thereby generating a verbal feature string inheriting a    further timestamp from the further timestamped audio waveform.-   36. The method (100) of one of the preceding embodiments, when    dependent on embodiment 2, wherein the at least one verbal response    (31) from the user is applied to a speech-to-text algorithm, thereby    generating a verbal feature string inheriting a further timestamp    from the timestamped audio waveform.-   37. The method (100) of embodiment 35 or 36, wherein determining    (155) the at least one contemporaneous feature (40) of the creative    work (10) based:    -   on the obtained at least one verbal response (31) from the user;        or    -   on the obtained at least one further verbal response (51) from        the user;    -   comprises applying the verbal feature string to a feature        recognition algorithm configured to identify a noun that most        likely relates to the at least one contemporaneous feature of        the creative work.-   38. The method (100) of embodiment 37, wherein identifying the at    least one contemporaneous feature (40) of the creative work (10)    comprises:    -   applying a text split algorithm configured to break the verbal        feature string into one or more subunits each comprising        sentences, phrases, clauses, and/or words; and    -   applying a noun extraction algorithm configured to perform a        look-up for one or more subunits in a generic dictionary        providing the at least one noun for the at least one        contemporaneous feature of the creative work; and    -   thereby identifying the noun that most likely relates to the at        least one contemporaneous feature of the creative work.-   39. The method (100) of embodiment 37 or 38, wherein the feature    recognition algorithm comprises at least one pre-trained machine    learning algorithm.-   40. The method (100) of one of the embodiments 37 to 39, wherein the    noun that most likely relates to the at least one contemporaneous    feature (40) of the creative work (10) inherits the further    timestamp of the verbal feature string.-   41. The method (100) of one of the embodiments 37 to 40, when    dependent on embodiment 32, wherein the at least one contemporaneous    feature (40) of the creative work (10), the noun that most likely    relates to the at least one contemporaneous feature of the creative    work and the further timestamp.-   42. The method (100) of one of the preceding embodiments, when    dependent on embodiment 2 or 3, further comprising generating (160)    at least one tailored response (60) based on the cognitive load (20)    and/or the corresponding contemporaneous feature (40).-   43. The method (100) of embodiment 42, wherein generating (160) the    at least one tailored response (60) is triggered by a request of a    user or of a further individual, or automatically when the cognitive    load (20) exceeds a predetermined cognitive load threshold.-   44. The method (100) of embodiment 42 or 43, further comprising    generating (161) at least one further tailored response (60) based    on at least one cognitive load (20) and/or the at least one    corresponding contemporaneous feature (40) queried from the score    database (220, 221),-   45. The method (100) of embodiment 44, wherein querying at least one    cognitive load (20) and/or the at least one corresponding    contemporaneous feature (40) can be subject to one or more    conditions comprising:    -   a condition restricting stored timestamps; and    -   a condition restricting stored cognitive loads; and    -   a condition restricting stored contemporaneous features.-   46. The method (100) of one of the embodiments 42 to 45, wherein the    at least one tailored response (60) is based on a tailored response    template (61).-   47. The method (100) of embodiment 46, wherein the tailored response    template (61) is a parametrizable text.-   48. The method (100) of embodiment 46 or 47, wherein generating    (160, 161) the at least one tailored response (60) based on the    cognitive load (20) and the corresponding contemporaneous feature    (40) comprises applying a tailored response algorithm configured to    integrate at least one cognitive load, the at least one    corresponding contemporaneous feature, and/or at least one    corresponding timestamp into the tailored response template (61).-   49. The method (100) of one of the preceding embodiments wherein a    verbal statement comprises a name of the user.-   50. The method (100) of one of the embodiments 42 to 49, comprising    outputting (170) the at least one tailored response via a user    interface (210) to the user.-   51. The method (100) of one of the embodiments 42 to 50, wherein the    at least one generated tailored response (60) is capable of    provoking a third response (62) from the user, and the method    further comprises:    -   prompting the user to vocally interact with the creative work        system (200) by vocalizing the at least one generated tailored        response (60); and    -   obtaining the third response (62) from the user via the user        interface (210); and    -   executing a third response algorithm based on the third response        (62) from the user, if the third response (62) from the user is        recognized to be affirmative.-   52. A creative work system (200) for measuring cognitive load (20)    of a user creating a creative work (10) comprising:    -   a user interface (210) comprising an audio interface (211);    -   wherein the creative work system is configured to run the method        (100) of one of the preceding embodiments.-   53. The creative work system (200) of embodiment 52, wherein the    audio interface (211) comprises at least one microphone (212) and at    least one speaker (213).-   54. The creative work system (200) of embodiment 52 or 53,    comprising access to at least one database (220) via a communication    channel (222).-   55. The creative work system (200) of one of the embodiments 52 to    54, comprising a camera (230) configured to record at least one    image or a sequence of images of the creative work (10) as creation    progresses.-   56. The creative work system (200) of one of the embodiments 52 to    55, wherein the user interface (210) comprises a graphical interface    (214).

REFERENCE NUMERALS

-   10 creative work-   20 cognitive load-   30 verbal statement capable of provoking at least one verbal    response from the user-   31 verbal response from the user-   31 predicted verbal response-   32 contemporaneous feature-   40 further verbal statement capable of provoking at least one    further verbal response from the user-   51 further verbal response from the user-   60 (further) tailored response-   61 a tailored response template-   62 third response-   100 computer-implemented method for measuring cognitive load of a    user creating a creative work in a creative work system-   110 generating at least one verbal statement capable of provoking at    least one verbal response from the user-   120 prompting the user to vocally interact with the creative work    system-   121 vocalizing the at least one generated verbal statement to the    user via an audio interface of the creative work system-   130 obtaining the at least one verbal response from the user via the    audio interface-   140 determining the cognitive load of the user based on the at least    one verbal response obtained from the user-   150 determining at least one contemporaneous feature of the creative    work based on the at least one obtained verbal response from the    user-   151 generating at least one further verbal statement capable of    provoking at least one further verbal response from the user-   152 prompting the user to vocally interact with the creative work    system-   153 vocalizing the at least one generated further verbal statement    to the user via an audio interface of the creative work system-   154 obtaining at least one further verbal response from the user via    the audio interface-   155 determining at least one contemporaneous feature of the creative    work based on the at least one obtained further verbal response from    the user-   160 generating at least one tailored response based on the cognitive    load and/or the corresponding contemporaneous feature-   161 generating at least one further tailored response based on at    least one cognitive load and/or the at least one corresponding    contemporaneous feature queried from the score database-   170 outputting the at least one tailored response via a user    interface to the user-   180 feeding-back information related to the at least one cognitive    load and/or the at least one corresponding contemporaneous feature    via the user interface-   200 creative work system-   210 user interface-   211 audio interface-   212 microphone-   213 speaker-   214 graphical interface-   220 database, candidate primitive database, response database, score    database, tailored response template database-   221 score database-   222 communication channel-   230 camera

1. A computer-implemented method for measuring cognitive load of a usercreating a creative work in a creative work system, comprising:generating at least one verbal statement capable of provoking at leastone verbal response from the user; prompting the user to vocallyinteract with the creative work system by vocalizing the at least onegenerated verbal statement to the user via an audio interface of thecreative work system; obtaining the at least one verbal response fromthe user via the audio interface; and determining the cognitive load ofthe user based on the at least one verbal response obtained from theuser, wherein generating the at least one verbal statement is based onat least one predicted verbal response suitable for determining thecognitive load of the user.
 2. The computer-implemented method of claim1, wherein generating the at least one verbal statement capable ofprovoking the at least one verbal response from the user comprises:applying a candidate verbal statement algorithm configured to generateone or more candidate verbal statements; applying a conversationalgorithm configured to generate for each candidate verbal statement oneor more predicted verbal responses and corresponding one or moreresponse probabilities, thereby generating, for each candidate verbalstatement, a list of predicted verbal responses and a vector of responseprobabilities RPV; applying a predicted verbal response assessmentalgorithm configured to assign a response score to each predicted verbalresponse, thereby generating, for each candidate verbal statement, avector of response scores; and applying a verbal response selectionalgorithm configured to select one of the one or more candidate verbalstatements based on the one or more vectors of response probabilitiesand on the one or more vectors of response scores, thereby generatingthe at least one verbal statement based on the at least one predictedverbal response suitable for determining the cognitive load of the user.3. The computer-implemented method of claim 2, wherein assigning theresponse score to each predicted verbal response comprises checking forone or more verbal features in each predicted verbal response andcomputing a feature score for each of the one or more verbal features.4. The computer-implemented method of claim 3, wherein the one or moreverbal feature comprises a verbal feature of a first type comprising: avariety of phoneme use; a variety of pseudo-syllable use; or a responselength.
 5. The computer-implemented method of claim 4, wherein checkingfor the one or more verbal features in each predicted verbal responseand computing the feature score for each of the one or more verbalfeatures comprises applying each predicted verbal response to a phonemeuse algorithm configured for: identifying at least one phoneme of thepredicted verbal response based on a predetermined list of phonemes;counting the phonemes of the predicted verbal response; counting uniquephonemes of the predicted verbal response; and dividing the count of theunique phonemes of the predicted verbal response by the count of thephonemes of the predicted verbal response, thereby computing a phonemescore, thereby computing the verbal feature score.
 6. Thecomputer-implemented method of claim 3, wherein assigning the responsescore to each predicted verbal response is based on at least one verbalfeature score corresponding to at least one verbal feature of thepredicted verbal response.
 7. The computer-implemented method of claim2, wherein selecting one of the one or more candidate verbal statementsbased on the one or more vectors of response probabilities and on theone or more vectors of response scores comprises: multiplying, for eachcandidate verbal statement, the vector of response probabilities RPV andthe vector of response scores, thereby generating, for each candidateverbal statement, a vector of weighted response scores; summing, foreach candidate verbal statement, components of the vector of weightedresponse scores, thereby generating, for each candidate verbalstatement, a total selection score; and selecting one of the one or morecandidate verbal statements with a highest total selection score.
 8. Thecomputer-implemented method of claim 1, wherein determining thecognitive load of the user based on the at least one verbal responseobtained from the user comprises assessing at least one vocal feature ofthe at least one verbal response obtained from the user.
 9. Thecomputer-implemented method of claim 8, wherein determining thecognitive load of the user based on the at least one verbal responseobtained from the user comprises applying one or more cognitive loadfeature assessment algorithms, wherein each cognitive load featureassessment algorithm corresponds to a vocal feature and is configuredfor: generating a vocal feature vector representation of thecorresponding vocal feature for the at least one verbal response fromthe user, thereby generating one or more vocal feature vectorrepresentations.
 10. The computer-implemented method of claim 9, whereindetermining the cognitive load of the user based on the at least oneverbal response obtained from the user comprises applying the one ormore vocal feature vector representations to a cognitive load scorealgorithm configured for: comparing each of the one or more vocalfeature vector representations to at least one predetermined benchmarkfor cognitive load, wherein each comparison comprises computing apredetermined criterion based on at least one vocal feature vectorrepresentation and the at least one predetermined benchmark forcognitive load; counting the one or more vocal feature vectorrepresentations satisfying a corresponding predetermined criteria;counting the one or more vocal feature vector representations; anddividing a count of the one or more vocal feature vector representationssatisfying the corresponding predetermined criteria by a count of theone or more vocal feature vector representations, thereby determiningthe cognitive load score of the user, thereby determining the cognitiveload (20) of the user.
 11. The computer-implemented method of claim 1,further comprising: determining at least one contemporaneous feature ofthe creative work based on the at least one obtained verbal responsefrom the user; and wherein the at least one verbal response from theuser is applied to a speech-to-text algorithm, thereby generating averbal feature string inheriting a further timestamp from a timestampedaudio waveform.
 12. The computer-implemented method of claim 11, whereindetermining the at least one contemporaneous feature of the creativework is based on the obtained at least one verbal response from the useror on the obtained at least one further verbal response from the user,and further comprising: applying the verbal feature string to a featurerecognition algorithm configured to identify a noun that most likelyrelates to the at least one contemporaneous feature of the creativework.
 13. The computer-implemented method of claim 11, furthercomprising: generating at least one tailored response based on thecognitive load and/or corresponding contemporaneous feature.
 14. Thecomputer-implemented method of claim 13, wherein the at least onetailored response is based on a tailored response template.
 15. A systemfor measuring cognitive load of a user creating a creative work in acreative work system, comprising: generating at least one verbalstatement capable of provoking at least one verbal response from theuser; prompting the user to vocally interact with the creative worksystem by vocalizing the at least one generated verbal statement to theuser via an audio interface of the creative work system; obtaining theat least one verbal response from the user via the audio interface; anddetermining the cognitive load of the user based on the at least oneverbal response obtained from the user, wherein generating the at leastone verbal statement is based on at least one predicted verbal responsesuitable for determining the cognitive load of the user.
 16. The systemof claim 15, wherein generating the at least one verbal statementcapable of provoking the at least one verbal response from the usercomprises: applying a candidate verbal statement algorithm configured togenerate one or more candidate verbal statements; applying aconversation algorithm configured to generate for each candidate verbalstatement one or more predicted verbal responses and corresponding oneor more response probabilities, thereby generating, for each candidateverbal statement, a list of predicted verbal responses and a vector ofresponse probabilities RPV; applying a predicted verbal responseassessment algorithm configured to assign a response score to eachpredicted verbal response, thereby generating, for each candidate verbalstatement, a vector of response scores; and applying a verbal responseselection algorithm configured to select one of the one or morecandidate verbal statements based on the one or more vectors of responseprobabilities and on the one or more vectors of response scores, therebygenerating the at least one verbal statement based on the at least onepredicted verbal response suitable for determining the cognitive load ofthe user.
 17. The system of claim 16, wherein assigning the responsescore to each predicted verbal response comprises checking for one ormore verbal features in each predicted verbal response and computing afeature score for each of the one or more verbal features.
 18. Thesystem of claim 17, wherein the one or more verbal features comprises averbal feature of a first type comprising: a variety of phoneme use; avariety of pseudo-syllable use; or a response length.
 19. The system ofclaim 18, wherein checking for the one or more verbal features in eachpredicted verbal response and computing the feature score for each ofthe one or more verbal features comprises applying each predicted verbalresponse to a phoneme use algorithm configured for: identifying at leastone phoneme of the predicted verbal response based on a predeterminedlist of phonemes; counting the phonemes of the predicted verbalresponse; counting unique phonemes of the predicted verbal response; anddividing the count of unique phonemes of the predicted verbal responseby the count of the phonemes of the predicted verbal response, therebycomputing a phoneme score, thereby computing the verbal feature score.20. An apparatus comprising: at least one processor; and at least onememory including computer program code for one or more programs, the atleast one memory and the computer program code configured to, with theat least one processor, cause the apparatus to perform at least thefollowing, generate at least one verbal statement capable of provokingat least one verbal response from a user; prompt the user to vocallyinteract with a creative work system by vocalizing the at least onegenerated verbal statement to the user via an audio interface of thecreative work system; obtain the at least one verbal response from theuser via the audio interface; and determine a cognitive load of the userbased on the at least one verbal response obtained from the user,wherein generating the at least one verbal statement is based on atleast one predicted verbal response suitable for determining thecognitive load of the user.